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  1. Proceedings of the Workshop on Education for High-Performance Computing (EduHPC '14)
  2. Development of a computational and data-enabled science and engineering Ph.D. program
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Using the pilot library to teach message-passing programming
Title Page iii
HPC/PDC immunization in the introductory computer science sequence
Copyright Page
PDC education in the BGU ECE department
Message from the Workshop Chair
Development of a computational and data-enabled science and engineering Ph.D. program
Trusted high-performance computing in the classroom
Teaching high performance computing: lessons from a flipped classroom, project-based course on finite element methods
Teaching parallelism without programming: a data science curriculum for non-CS students
The blue waters student internship program: promoting competence and confidence for next generation researchers in high-performance computing
Teaching parallel programming using Java
Author Index

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Development of a computational and data-enabled science and engineering Ph.D. program

Content Provider ACM Digital Library
Author Jones, Matthew Bauman, Paul T. Patra, Abani Chandola, Varun
Abstract The previous two decades have seen the successful deployment of Computational Science programs in universities across the globe. These programs are aimed at training scientists and engineers to tackle problems requiring interdisciplinary approaches to finding solutions to scientific and engineering problems and the development of new computing, as exemplified by the co-design approach to exascale architectures and applications. Thus, the programs emphasize preparation in applied mathematics, numerical analysis, and scientific computing in addition to science and engineering work relevant to the target application. The rise of so-called "Big-Data" applications and the use of large data in business decision support and even in computational science workflows like uncertainty analysis are driving a need for training in data sciences. This paper makes the argument that, rather than treating topics in machine learning, statistics, etc. as stand-alone fields of study that students learn as electives, data-science should be an integral part of interdisciplinary training for future researchers. This approach is at the core of the newly developed Computational and Data-Enabled Science and Engineering (CDSE) Ph.D. program at the University of Buffalo. This paper describes the development of the Ph.D. program, the target student audience, and strategies for effectively executing the proposed curriculum.
Starting Page 21
Ending Page 26
Page Count 6
File Format PDF
ISBN 9781479970216
DOI 10.1109/EduHPC.2014.8
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-16
Access Restriction Subscribed
Content Type Text
Resource Type Article
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